Many fields encounter problems associated with perceptually tuning a system. For example, in perceptually tuning or “fitting” a hearing assistance device, such as a hearing aid, antiquated methods subjected a single hearing impaired user to many and various audio-related settings of their hearing aid and, often via technical support from an audiologist, individually determined the preferred settings for that single user. This approach, however, has proven itself lacking in universal applicability.
Thus, prescriptive fitting formulas have evolved whereby large numbers of users can become satisfactorily fit by adjusting the same hearing assistance device. With the advent of programmable hearing aids, this approach has become especially more viable. This approach is, however, still too general because individual preferences are often ignored. In one particular hearing assistance device fitting selection strategy, paired comparisons were used. In this strategy, users were presented with a choice between two actual hearing aids from a large set of hearing aids and asked to compare them in an iterative round robin, double elimination tournament or modified simplex procedure until one hearing aid “winner” having optimum frequency-gain characteristics was converged upon. These uses of paired comparisons, however, are extremely impractical in time and financial resources. Moreover, such strategy cannot easily find implementation in an unsupervised home setting by an actual hearing aid user.
In a more recent and very limited selection strategy, genetic algorithms were blended with user input to achieve a hearing aid fitting. As is known, and as its name implies, genetic algorithms are a class of algorithms modeled upon living organisms' ability to ensure their evolutionary success via natural selection. In natural selection, the fittest organisms survive while the weakest are killed off. The next generation of organisms (children) are, thus, offspring of the fittest previous generation (parents). The algorithms also provide for mutations as insurance against the development of a relatively unchanging population incapable of continued evolution.
In breeding children or offspring in a genetic algorithm, “crossover” operators are applied to parent genes. In essence, two parent bit strings (ones and zeroes, for example) from the algorithm are crossed at a crossover point and the children are given attributes of each parent. “Mutation” operators are also applied to a relatively smaller number of parent bit strings, typically by replacing ones with zeroes and vice versa. Both crossover and mutation closely model biological behavior where parent chromosomes line up and crossover thereby swapping portions of their genetic code or become mutated.
What is needed in the art is a better and simpler selection strategy for fitting or tuning hearing assistance devices to individual users' preferred settings. The art needs better genetic algorithm operations for perceptually tuning a system having many interacting parameters, and including subjective user input.